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Data Processing Languages for Business Intelligence. SQL vs. R

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  • Marin FOTACHE

Abstract

As data centric approach, Business Intelligence (BI) deals with the storage, integration, processing, exploration and analysis of information gathered from multiple sources in various formats and volumes. BI systems are generally synonymous to costly, complex platforms that require vast organizational resources. But there is also an-other face of BI, that of a pool of data sources, applications, services developed at different times using different technologies. This is “democratic†BI or, in some cases, “fragmented†, “patched†(or “chaotic†) BI. Fragmentation creates not only integration problems, but also supports BI agility as new modules can be quickly developed. Among various languages and tools that cover large extents of BI activities, SQL and R are instrumental for both BI platform developers and BI users. SQL and R address both monolithic and democratic BI. This paper compares essential data processing features of two languages, identifying similarities and differences among them and also their strengths and limits.

Suggested Citation

  • Marin FOTACHE, 2016. "Data Processing Languages for Business Intelligence. SQL vs. R," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 20(1), pages 48-61.
    • Handle: RePEc:aes:infoec:v:20:y:2016:i:1:p:48-61
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    References listed on IDEAS

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    1. Octavian DOSPINESCU & Marian PERCA, 2013. "Web Services in Mobile Applications," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 17-26.
    2. Kane, Michael & Emerson, John W. & Weston, Stephen, 2013. "Scalable Strategies for Computing with Massive Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i14).
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